Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning

Xuechen CHEN; Jiaxuan YI; Aixiang WANG; Xiaoheng DENG

doi:10.11959/j.issn.2096-3750.2024.00358

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Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning

Theory and Technology | 更新时间：2024-06-05

- Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning
- Chinese Journal on Internet of Things Vol. 8, Issue 1, Pages: 40-48(2024)
- 作者机构：
  
  1. 中南大学电子信息学院，湖南长沙 410004
  2. 中南大学计算机学院，湖南长沙 410083
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00358
  CLC： TN915.08
- Revised：2024-01-24，
  
  Online First：2024-03，
  
  Published：30 March 2024
- 稿件说明：
移动端阅览
Xuechen CHEN, Jiaxuan YI, Aixiang WANG, et al. Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning[J]. Chinese Journal on Internet of Things, 2024, 8(1): 40-48.
DOI：

Xuechen CHEN, Jiaxuan YI, Aixiang WANG, et al. Multi-data fusionaided indoor localization based on continuous action space deep reinforcement learning[J]. Chinese Journal on Internet of Things, 2024, 8(1): 40-48. DOI： 10.11959/j.issn.2096-3750.2024.00358.

摘要

基于智能手机的室内定位在研究和工业领域都引起了相当大的关注。然而在复杂的定位环境中，定位的准确性和鲁棒性仍然是具有挑战性的问题。考虑到行人航位推算（PDR

pedestrian dead reckoning）算法被广泛配备在最近的智能手机上，提出了一种基于双延迟深度确定性策略梯度（TD3

twin delayed deep deterministic policy gradient）的室内定位融合方法，该方法集成了Wi-Fi信息和PDR数据，将PDR的定位过程建模为马尔可夫过程并引入了智能体的连续动作空间。最后，与3个最先进的深度Q网络（DQN

deep Q network）室内定位方法进行实验。实验结果表明，该方法能够显著减少定位误差，提高定位准确性。

Abstract

Significant attention has been paid to indoor localization using smartphones in both research and industry.However

the accuracy and robustness of localization remain challenging issues

particularly in complex indoor environments.In light of the prevalent incorporation of pedestrian dead reckoning (PDR) devices in contemporary smartphones

an advanced indoor localization fusion method

anchored in the twin delayed deep deterministic policy gradient (TD3) framework

was proposed.In this approach

a seamless integration of Wi-Fi information and PDR data was achieved.The localization process of PDR was modeled as a Markov process

and a comprehensive continuous action space was introduced for the agent.To evaluate the performance of the proposed method

experiments were conducted and this approach was compared with three state-of-the-art deep Q network (DQN) based indoor localization methods.The experimental results demonstrate that the proposed method significantly reduces localization errors and enhances overall localization accuracy.

关键词

Keywords

references

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